Investigation of low cycle fatigue on large gas turbine ...€¦ · Investigation of low cycle...
Transcript of Investigation of low cycle fatigue on large gas turbine ...€¦ · Investigation of low cycle...
Investigation of low cycle fatigue on large gas turbine casings
under consideration of geometric tolerances
Untersuchung der Lebensdauer von Gasturbinengehäusen unter
Berücksichtigung geometrischer Streuungen
siemens.com/powergeneration
Uwe Lohse Siemens AG Large Gas Turbines; Burkhard Voss Siemens AG Large Gas Turbines; ThorstenLowitz Siemens AG Large Gas Turbines
Holger Schulze Spüntrup ITB Dortmund; Sebastian Wolff DYNARDO Austria
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Where we are – Gas Turbine Locations & JointVenture Partners
JV location
Gas Turbines location
Muelheim, Germany
SGTT,
St. Petersburg, Russia
SEPG/SGTP,
Shanghai, China
Erlangen, Germany
Charlottesville, USA
Gurgaon, India
Shanghai, China
Jupiter, USAOrlando, USA
Charlotte Plant, USA
Berlin Plant, Germany
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The Siemens gas turbines portfolio:The right engine for every requirement
Heavy-dutygas turbines
Industrialgas turbines
Aeroderivativegas turbines
50H
z50
Hz
or60
Hz
60H
z425 MW
329 MW
187 MW
310 MW
250 MW
117 MW
53 to 66 / 54 to 62 MW
27 to 33 / 28 to 34 MW
4 to 7 MW
48 to 54 MW
40 / 41 MW
33 / 34 MW
24 / 25 MW
13 to 14 / 13 to 15 MW
8 / 8 to 9 MW
5 / 6 MW
SGT5-8000H
SGT5-4000F
SGT5-2000E
SGT6-8000H
SGT6-5000F
SGT6-2000E
SGT-A65 TR
SGT-800
SGT-A45 TR
SGT-750
SGT-700
SGT-A30 RB andSGT-A35 RB
SGT-600
SGT-400
SGT-300
SGT-100
SGT-A05 AE
39 to 44 MW
Power Generation / Mechanical Drive, Performance at ISO conditions
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SGT5-8000H during assembly at Berlin plant
Efficiency:ƒ GT > 40 %
ƒ GUD 61 %
With district heating~85 %
Power:• GT 425 MW
• CCPP 630 MW
Weight: ~445 t
Length: ~12,6 m
Diameter: ~5,5 m
Fleet: > 74 units
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SGT5-8000H Plant Lausward DuesseldorfPress Release
Das neue Erdgaskraftwerk ist am Netz
Das Gas- und Dampfkraftwerk ist ein Meister derWiederverwertung von Energie. DieReibungsverluste, die bei der Umwandlung vonEnergie in Strom auftreten sind deshalb geringerals bei anderen Kraftwerken. Durch dasZusammenspiel mit Fernwärme erreicht dasKraftwerk einen Wirkungsgrad von 85 Prozent(bei Steinkohlekraftwerken liegt der Wert bei 45Prozent). Dank umweltschonender Technikwerden im ersten Betriebsjahr 600.000 TonnenCO2 eingespart, im Jahr 2025 sollen es übereine Million Tonnen sein.
Source: RP 2016-01-29
Düsseldorf. Siemens hat am 22. Januar2016 das schlüsselfertig errichtete Gas- undDampfturbinen (GuD)-Kraftwerk amStandort Lausward im Düsseldorfer Hafenan den Kunden und Betreiber StadtwerkeDüsseldorf AG übergeben. Das Kraftwerkwartet gleich mit drei neuen Rekorden imweltweiten Vergleich auf. In einer Testfahrtvor Abnahme wurde im Block „Fortuna“ einemaximale elektrische Leistung von 603,8Megawatt (MW) erreicht, das ist eine neueBestmarke für ein GuD-Kraftwerk dieserAuslegung. Gleichzeitig wurde einWirkungsgrad von rund 61,5 Prozentnachgewiesen – ein neuer Weltrekord.
Source:
WAZ
2016-01-28
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Key thermodynamic values for bolting design
-40°C +50°C
~440°C >10 bar
~580° to ~650°C
Compressor section
Combustion section
Turbine section
Exhaust section
SUCK
SQUEEZEBANG
BLOW
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Motivation
Customer requirements
• Effective• Reliable• Available• Flexible• Cost effective
Pushing design closer to the limits to fulfillthe customer requirements
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Motivation
With a classical deterministic approach it is difficult to fulfillthe customer needs.Safety factors for every impacted parameter used• Material distribution• Boundary condition• Geometry variation
Probabilistic approach helps to fulfill the customerrequirements
Only a probabilistic approach can considerthe interaction of the impacted parameters
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Effects on structural reliability / availability
This project deals only with thegeometry’s effect
MaterialMaterial scatter of properties and fatigue curveSize effect also known as weakest link theory
GeometryManufacturing tolerances
LoadingTransientEnvironmentOperation
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The idea
Quantify geometry scatter and assess the effects on thestructural behavior and design life
How to:• 3D-Scan existing geometries• Compare scanned and nominal geometry• Get the deviation of the actual geometry from the nominal geometry• Apply the deviation on a FE-Model• Compare scanned and nominal geometry and their utilization
Repeat for many different deviated geometries to obtainrepresentative data
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Challenges on scanning
• Huge amount of nodes from 180°scanNodes > 23.000.000
• Limited access for one scanner position due to the size of the object
• Limited time to scan in the production process
• Limited sample size due to long lead times
• Capability to repair and defeature scans required(actual with GEOMAGIC or GOM possible)
Improvement on scanning technic andtime wishful for daily user
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Impression of laser scan measurement
Photogrammetry system GOM Tritop incombination with the GOM ATOS Triple Scan –Blue Light Scanner.
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Challenges on structure mechanic
Actual solution: use a sub model of the90°FEA model
3D FEA including bolting contact and thermal transient required for realistic stressesfor LCF assessment
840.000 Nodes380.000 Elements
>30 GB Disc space required~ 3 Days on medium HPC hardware⇑ Project time to run ~100 samples in robust analysis not feasible.
⇑ Disadvantage! Loss of global secondary membrane and bending effect on submodel.
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Challenges on structure mechanic
Trade-off between calculation time andresult quality
240.000 Nodes
~7 GB Disc space per sample required~ 90 min on medium HPC hardware⇑ ~6 Days to run ~100 samples
840.000 Nodes
>30 GB Disc space required~ 3 Days on medium HPC hardware⇑ Project time to run ~100 samples
in robust analysis not feasible.
Submodel
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Example of the sub Modell
Not machined surface.Casting tolerance according ISO 8062
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Statistics on Structures – How it works
Statistics on Structures(SoS)
Scan Nominal geometry
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Statistics on Structures – How it works
FE-Modell
Scan1 Sample = 1 Scalar value
for the geometry deviationper node and per scan
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Example of scanning results based on 8 samples
Measured perpendicular to the surface coordinate; Normal vector direction
++
++
++
-
Mean deviation
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Example of scanning results based on 8 samples
Standard deviation
Measured perpendicular to the surface coordinate; Normal vector directionBasic statistic quantities can already beobtained
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Example of scanning results based on 8 samples
Casting tolerances according ISO 8062-3
Tolerance class n+1
Tolerance class n
Absolute deviation: Mean+3∙Sigma (~99.73%)
Basic quantities show critical locations forquality checks
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Method to work with limited sample size
Empiric random field > 100 scans
Semi synthetic field ~5 scans used to calculate meanvalue and standard deviation. Field simulated at randomwith mean an STDDEV from small number of scans
Full synthetic Random simulations based on experiencesfrom other projects or local CTQ records (critical to quality):CTQ are standard of the quality process but only local andcan‘t include the full field information.
Decreased effort tocollect data
Decreased effort toretrieve high quality
results
Compromise between effort to collect data and the resultquality needs to be discussed between the involved
parties before the project starts.
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Semi synthetic field
Mean deviation
Standard deviation
=
Spectraldecomposition
Linear combination of mean value and randomly scaled scatter shapes
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It‘s possible to create an arbitrary amountof different geometry variations
Semi synthetic field
…
≈
≈ +
+ ∙ (+1,5)∙ (−1,5)
Linear combination of mean value and randomly scaled scatter shapes
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Workflow / Theory
SoS
Scan
Model
n Designpointswith n Solutions
Post-
processing
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Workflow / Theory
SoS
Post-
processing
Mean temperature Standard deviation of temperature
SoS
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Workflow / Theory
SoS
Post-
processing
SoS
=
Spectraldecomposition
Geometry deviation Temperature distribution
Scat
terS
hape
1Sc
atte
rSha
pe2
F-CoP
Correlation of geometry scatter andtemperature scatter
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Workflow / Theory
SoS
Post-
processing
SoS
LCF-
Evaluation
Mean design life
Similarly, the workflow allows correlationof geometry scatter and design life scatter
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Design Life
Mean design life
Considering geometry scatter shows increased as wellas decreased design life⇓ Assessment of nominal
geometry is globally a good approach
−
> + 5%
< - 5%
Design life difference dueto probabilstic approach
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Design Life
Mean design life
However, local scatter of design life can only beconsidered by probabilistic approach and may lead to
different highly utilized locations
: =
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Design Life
Mean design life
− ∙− ∙
-3σ Design lifeMinimal values for 99.7% of real geometries
However, local scatter of design life can only beconsidered by probabilistic approach and may lead to
different highly utilized locations
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Summary
• Synthetic random fields allow probabilistic assessment of structures with few availablescans
• Software allows correlation of geometry scatter and result scatter
• Correlation of input and output allows identification of locations of uncritical geometryscatter with respect to design life. Tolerances can be adjusted accordingly.
• Easy statistical evaluation of the results plotted directly on the model in SoS
• Due to consideration of geometry scatter according safety factors may be adjusted
• Still some work to do...• Increase of sample size• Automatization of process• ...
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Contact and Acknowledgement
Uwe Lohse [email protected] Voss [email protected] Lowitz [email protected] Wolff [email protected] Schulze Spüntrup [email protected]
The presentation is based on the master thesis of Holger Schulze Spüntrup at Fachhochschule Dortmund, University ofApplied SciencesReferent Prof. Marius Geller, Fachhochschule DortmundCo-referent Sebastian Wolff, DYNARDO Austria GmbHTechnical support Dr. Frank Bremer
ITB
Pictures & logos taken from: www.dynardo.de and www.ansys.com
Ingenieurgesellschaft für technische Berechnungen mbHEuropaplatz 7, 44269 DortmundTel.: +49 (0)231 94 53 65 - 21Fax: +49 (0)231 94 53 65 - 11Geschäftsführer: Dr. Frank BrehmerHandelsregister Dortmund: HRB 16440
www.itb-fem.de
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Thanks for your attention!
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Disclaimer
This document contains forward-looking statements and information – that is, statements related to future, not past,events. These statements may be identified either orally or in writing by words as “expects”, “anticipates”, “intends”,“plans”, “believes”, “seeks”, “estimates”, “will” or words of similar meaning. Such statements are based on our currentexpectations and certain assumptions, and are, therefore, subject to certain risks and uncertainties. A variety of factors,many of which are beyond Siemens’ control, affect its operations, performance, business strategy and results and couldcause the actual results, performance or achievements of Siemens worldwide to be materially different from any futureresults, performance or achievements that may be expressed or implied by such forward-looking statements. For us,particular uncertainties arise, among others, from changes in general economic and business conditions, changes incurrency exchange rates and interest rates, introduction of competing products or technologies by other companies,lack of acceptance of new products or services by customers targeted by Siemens worldwide, changes in businessstrategy and various other factors. More detailed information about certain of these factors is contained in Siemens’filings with the SEC, which are available on the Siemens website, www.siemens.com and on the SEC’s website,www.sec.gov. Should one or more of these risks or uncertainties materialize, or should underlying assumptions proveincorrect, actual results may vary materially from those described in the relevant forward-looking statement asanticipated, believed, estimated, expected, intended, planned or projected. Siemens does not intend or assume anyobligation to update or revise these forward-looking statements in light of developments which differ from thoseanticipated.
Trademarks mentioned in this document are the property of Siemens AG, it's affiliates or their respective owners.
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